How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration

!pip install pygwalker pandas numpy scikit-learn


import pandas as pd
import numpy as np
import pygwalker as pyg
from datetime import datetime, timedelta

def generate_advanced_dataset():
   np.random.seed(42)
   start_date = datetime(2022, 1, 1)
   dates = [start_date + timedelta(days=x) for x in range(730)]
   categories = ['Electronics', 'Clothing', 'Home & Garden', 'Sports', 'Books']
   products = {
       'Electronics': ['Laptop', 'Smartphone', 'Headphones', 'Tablet', 'Smartwatch'],
       'Clothing': ['T-Shirt', 'Jeans', 'Dress', 'Jacket', 'Sneakers'],
       'Home & Garden': ['Furniture', 'Lamp', 'Rug', 'Plant', 'Cookware'],
       'Sports': ['Yoga Mat', 'Dumbbell', 'Running Shoes', 'Bicycle', 'Tennis Racket'],
       'Books': ['Fiction', 'Non-Fiction', 'Biography', 'Science', 'History']
   }
   n_transactions = 5000
   data = []
   for _ in range(n_transactions):
       date = np.random.choice(dates)
       category = np.random.choice(categories)
       product = np.random.choice(products[category])
       base_prices = {
           'Electronics': (200, 1500),
           'Clothing': (20, 150),
           'Home & Garden': (30, 500),
           'Sports': (25, 300),
           'Books': (10, 50)
       }
       price = np.random.uniform(*base_prices[category])
       quantity = np.random.choice([1, 1, 1, 2, 2, 3], p=[0.5, 0.2, 0.15, 0.1, 0.03, 0.02])
       customer_segment = np.random.choice(['Premium', 'Standard', 'Budget'], p=[0.2, 0.5, 0.3])
       age_group = np.random.choice(['18-25', '26-35', '36-45', '46-55', '56+'])
       region = np.random.choice(['North', 'South', 'East', 'West', 'Central'])
       month = date.month
       seasonal_factor = 1.0
       if month in [11, 12]:
           seasonal_factor = 1.5
       elif month in [6, 7]:
           seasonal_factor = 1.2
       revenue = price * quantity * seasonal_factor
       discount = np.random.choice([0, 5, 10, 15, 20, 25], p=[0.4, 0.2, 0.15, 0.15, 0.07, 0.03])
       marketing_channel = np.random.choice(['Organic', 'Social Media', 'Email', 'Paid Ads'])
       base_satisfaction = 4.0
       if customer_segment == 'Premium':
           base_satisfaction += 0.5
       if discount > 15:
           base_satisfaction += 0.3
       satisfaction = np.clip(base_satisfaction + np.random.normal(0, 0.5), 1, 5)
       data.append({
           'Date': date, 'Category': category, 'Product': product, 'Price': round(price, 2),
           'Quantity': quantity, 'Revenue': round(revenue, 2), 'Customer_Segment': customer_segment,
           'Age_Group': age_group, 'Region': region, 'Discount_%': discount,
           'Marketing_Channel': marketing_channel, 'Customer_Satisfaction': round(satisfaction, 2),
           'Month': date.strftime('%B'), 'Year': date.year, 'Quarter': f'Q{(date.month-1)//3 + 1}'
       })
   df = pd.DataFrame(data)
   df['Profit_Margin'] = round(df['Revenue'] * (1 - df['Discount_%']/100) * 0.3, 2)
   df['Days_Since_Start'] = (df['Date'] - df['Date'].min()).dt.days
   return df

print("Generating advanced e-commerce dataset...")
df = generate_advanced_dataset()
print(f"nDataset Overview:")
print(f"Total Transactions: {len(df)}")
print(f"Date Range: {df['Date'].min()} to {df['Date'].max()}")
print(f"Total Revenue: ${df['Revenue'].sum():,.2f}")
print(f"nColumns: {list(df.columns)}")
print("nFirst few rows:")
print(df.head())

daily_sales = df.groupby('Date').agg({
   'Revenue': 'sum', 'Quantity': 'sum', 'Customer_Satisfaction': 'mean'
}).reset_index()


category_analysis = df.groupby('Category').agg({
   'Revenue': ['sum', 'mean'], 'Quantity': 'sum', 'Customer_Satisfaction': 'mean', 'Profit_Margin': 'sum'
}).reset_index()
category_analysis.columns = ['Category', 'Total_Revenue', 'Avg_Order_Value',
                            'Total_Quantity', 'Avg_Satisfaction', 'Total_Profit']


segment_analysis = df.groupby(['Customer_Segment', 'Region']).agg({
   'Revenue': 'sum', 'Customer_Satisfaction': 'mean'
}).reset_index()


print("n" + "="*50)
print("DATASET READY FOR PYGWALKER VISUALIZATION")
print("="*50)

print("n Launching PyGWalker Interactive Interface...")
walker = pyg.walk(
   df,
   spec="./pygwalker_config.json",
   use_kernel_calc=True,
   theme_key='g2'
)


print("n PyGWalker is now running!")
print(" Try creating these visualizations:")
print("   - Revenue trend over time (line chart)")
print("   - Category distribution (pie chart)")
print("   - Price vs Satisfaction scatter plot")
print("   - Regional sales heatmap")
print("   - Discount effectiveness analysis")